Multitarget tracking with multiscan knowledge exploitation using sequential MCMC sampling
Exploitation of external knowledge through constrained filtering guarantees improved performance. In this paper we show how multiscan processing of such information further enhances the track accuracy. This can be achieved using a Fixed-Lag Smoothing procedure, and a proof of improvement is given in...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/38605 |
| _version_ | 1848755365733203968 |
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| author | Bocquel, M. Papi, Francesco Podt, M. Driessen, H. |
| author_facet | Bocquel, M. Papi, Francesco Podt, M. Driessen, H. |
| author_sort | Bocquel, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Exploitation of external knowledge through constrained filtering guarantees improved performance. In this paper we show how multiscan processing of such information further enhances the track accuracy. This can be achieved using a Fixed-Lag Smoothing procedure, and a proof of improvement is given in terms of entropy reduction. Such multiscan algorithm, i.e., named KB-Smoother ('Fixed-lag smoothing for Bayes optimal exploitation of external knowledge,' F. Papi , Proc. 15th Int. Conf. Inf. Fusion, 2012) can be implemented by means of a SIR-PF. In practice, the SIR-PF suffers from depletion problems, which are further amplified by the Smoothing technique. Sequential MCMC methods represent an efficient alternative to the standard SIR-PF approach. Furthermore, by borrowing techniques from genetic algorithms, a fully parallelizable multitarget tracker can be defined. Such approach, i.e., named Interacting Population (IP)-MCMC-PF, was first introduced in 'Multitarget tracking with interacting population-based MCMC-PF' (M Bocquel , Proc. 15th Int. Conf. Inf. Fusion, 2012). In this paper, we propose and analyze a combination of the KB-Smoother along with the IP-MCMC-PF. As will be shown, the combination of the two methods yields an improved track accuracy while mitigating the loss of particles diversity. Simulation analyses for single and multitarget tracking scenarios confirm the benefits of the proposed approach. © 2007-2012 IEEE. |
| first_indexed | 2025-11-14T08:55:09Z |
| format | Journal Article |
| id | curtin-20.500.11937-38605 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:55:09Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-386052017-09-13T14:18:23Z Multitarget tracking with multiscan knowledge exploitation using sequential MCMC sampling Bocquel, M. Papi, Francesco Podt, M. Driessen, H. Exploitation of external knowledge through constrained filtering guarantees improved performance. In this paper we show how multiscan processing of such information further enhances the track accuracy. This can be achieved using a Fixed-Lag Smoothing procedure, and a proof of improvement is given in terms of entropy reduction. Such multiscan algorithm, i.e., named KB-Smoother ('Fixed-lag smoothing for Bayes optimal exploitation of external knowledge,' F. Papi , Proc. 15th Int. Conf. Inf. Fusion, 2012) can be implemented by means of a SIR-PF. In practice, the SIR-PF suffers from depletion problems, which are further amplified by the Smoothing technique. Sequential MCMC methods represent an efficient alternative to the standard SIR-PF approach. Furthermore, by borrowing techniques from genetic algorithms, a fully parallelizable multitarget tracker can be defined. Such approach, i.e., named Interacting Population (IP)-MCMC-PF, was first introduced in 'Multitarget tracking with interacting population-based MCMC-PF' (M Bocquel , Proc. 15th Int. Conf. Inf. Fusion, 2012). In this paper, we propose and analyze a combination of the KB-Smoother along with the IP-MCMC-PF. As will be shown, the combination of the two methods yields an improved track accuracy while mitigating the loss of particles diversity. Simulation analyses for single and multitarget tracking scenarios confirm the benefits of the proposed approach. © 2007-2012 IEEE. 2013 Journal Article http://hdl.handle.net/20.500.11937/38605 10.1109/JSTSP.2013.2251317 restricted |
| spellingShingle | Bocquel, M. Papi, Francesco Podt, M. Driessen, H. Multitarget tracking with multiscan knowledge exploitation using sequential MCMC sampling |
| title | Multitarget tracking with multiscan knowledge exploitation using sequential MCMC sampling |
| title_full | Multitarget tracking with multiscan knowledge exploitation using sequential MCMC sampling |
| title_fullStr | Multitarget tracking with multiscan knowledge exploitation using sequential MCMC sampling |
| title_full_unstemmed | Multitarget tracking with multiscan knowledge exploitation using sequential MCMC sampling |
| title_short | Multitarget tracking with multiscan knowledge exploitation using sequential MCMC sampling |
| title_sort | multitarget tracking with multiscan knowledge exploitation using sequential mcmc sampling |
| url | http://hdl.handle.net/20.500.11937/38605 |